Torque Control Of Induction Motor Research Articles

A Simple Online Tuning Flux Weighting Factor in Finite Control Set – Predictive Torque Control for Induction Motor Drive

The Finite Control Set-Predictive Torque Control (FSC-PTC) is a practical and intuitive method to control an Induction Motor (IM). However, the challenge of this technique is the difficulty of tuning the weighting factor so that it performs optimally at all speeds. Hence, this paper proposes a new method for improving the optimization technique by automatically tuning the weighting factor using a flux controller. The flux controller is represented as a DC gain and designed to be proportional to the absolute value of the predicted stator flux error. This method eliminates the need for complex computation to select the suitable weighting factor while also efficiently regulating flux and torque. The proposed method is experimentally validated on a DS1104 controller board and compared to a constant flux weighting factor method that was tested under speed conditions. The results demonstrate that the new method can further decrease the torque ripple compared to the constant weighting factor while maintaining the flux ripple and harmonic distortion limits.

Direct torque control of induction motor based on double-power-super-twisting sliding mode speed control for electric vehicle applications

Direct torque control of induction motor based on double-power-super-twisting sliding mode speed control for electric vehicle applications

Optimizing electric vehicle powertrains peak performance with robust predictive direct torque control of induction motors: a practical approach and experimental validation

Enhancing the efficiency of the electric vehicle’s powertrain becomes a crucial focus, wherein the control system for the traction motor plays a significant role. This paper presents a novel electric vehicle traction motor control system based on a robust predictive direct torque control approach, an improved version of the conventional DTC, where the traditional switching table and the hysteresis regulators are substituted with a predictive block based on an optimization algorithm. Additionally, a robust predictive speed loop regulator is employed instead of the proportional-integral regulator, which integrates a new cost function with a finite horizon, incorporating integral action into the control law based on a Taylor series expansion. This technique’s primary benefit is its independence from the necessity to measure and observe external disturbances, as well as uncertainties related to parameters. The effectiveness of the suggested system was confirmed through simulation and experimental results under the OPAL-RT platform. The findings indicate that the proposed approach outperforms the conventional method in terms of rejecting disturbances, exhibiting robustness to variations in parameters, and minimizing torque ripple.

Direct torque control of induction motor based on sliding mode

Direct torque control of induction motor based on sliding mode

Predictive torque control of induction motor for rotor bar faults diagnosis

Unlike DC motors and synchronous motors, they are maintenance-free motors due to the absence of brushes, commutators and slip rings. Induction motors can be operated in polluted and explosive environments as they do not have brushes which can cause sparks. In this paper, the performance of two control techniques, namely direct torque control (DTC) and predictive torque control (PTC), are compared in transient and static states when applied to a faulty induction machine (IM). The current and torque ripples is evaluated in a healthy machine, as well as in the presence of faults, at various speed and load values. During the transient state, the objective is to assess the method that provides the optimal dynamic response, which is achieving the desired speed without any overshooting while during the static state, the objective is to minimize torque ripple and harmonics in the stator current. The Discrete Wavelet Transform (DWT) is used to analyze stator phase current. In addition, the energy eigen value (EEV) analysis has been used to determine the fault severity. The healthy and faulty systems are simulated using Matlab/Simulink for the two control methods. The results show the superiority of the PTC method compared to the DTC. A comparison of the proposed control method with other works reported in the literature is performed to verify the superiority of the proposed strategy.

Optimized fractional order PID controller with sensorless speed estimation for torque control in induction motor

With rising concern on environmental protection, the exploitation of EV has been widely focused. The IM is said to be the best in comparing varied electric motors for EV applications due to its lower price and robustness. In addition, sensor-less controlling schemes like DTC and FOC are found to be appropriate for crucial appliances owing to their energy savings, improved efficiency, and reliability. The “Optimized Fractional Order Proportional Integral Controller (FoPID)” with a sensor-less speed estimate mechanism is proposed in this work. Additionally, the parameters of FoPID are adjusted using a brand-new hybrid method called the Wolf based Elephant Herding method (W-EHA), which combines the ideas of Grey wolf optimization (GWO) model and Elephant herding optimization (EHO). The superiority of the recommended technique is finally shown in terms of controller performance and error. The RMSE obtained by the developed model at speed = 100 is 3.344, which is 53.89 %, 24.22 %, 1.09 %, and 38.96 % superior to existing approaches like P-controller, MFI-DA + FoPID, EHO-FoPID, and GWO-FoPID respectively.

Torque ripple reduction with modified torque comparator in direct torque-controlled induction motor

Torque ripple reduction with modified torque comparator in direct torque-controlled induction motor

Super-Twisting MRAS Observer-Based Non-linear Direct Flux and Torque Control for Induction Motor Drives

Abstract This research paper proposes a novel design of an efficient combined sliding mode observer (SMO) for induction motor flux and speed estimation. The suggested sensorless technique employs the sliding mode’s second-order approach using a model reference adaptive system (MRAS). The second-order super-twisting control method is free-chattering, which lowers the chattering effect while preserving the same good features as the first-order sliding mode control (SMC). In addition, the conjunction with the MRAS as a separated speed estimator can raise the accuracy and make the observer immune to speed fluctuations, particularly for low-speed applications. Furthermore, in order to achieve effective decoupled flux–torque control, the super-twisting algorithm (STA) was combined with a non-linear feedback linearisation controller for the inner control loop construction. This strategy can boost the control system’s stability and robustness against external disturbances and modelling uncertainty. The performance analysis of the suggested methods has been carried out via simulation and experimental validation utilizing MATLAB/Simulink with the dSpace 1104 real-time interface.

Improved Direct Torque Control of Induction Motor in MATLAB

Improved Direct Torque Control of Induction Motor in MATLAB

Low-Cost Direct Torque Control for Induction Motor Using Single DC-Link Current Sensor

Low-Cost Direct Torque Control for Induction Motor Using Single DC-Link Current Sensor

Teamwork optimization based DTC for enhanced performance of IM based electric vehicle

The tailpipe emissions caused by vehicles using internal combustion engines are a significant source of air pollution. To reduce the health hazards caused by air pollution, advanced countries are now adopting the use of electric vehicles (EVs). Due to the advancement of electric vehicles, research and development efforts are being made to improve the performance of EV motors. With a nominal reference stator flux, the classical induction motor drive generates significant flux, torque ripple, and current harmonics. In this work, a teamwork optimization algorithm (TOA)-based optimal stator flux strategy is suggested for torque ripple reduction applied in a classical direct torque-controlled induction motor drive. The suggested algorithm's responsiveness is investigated under various steady-state and dynamic operating conditions. The proposed DTC-IM drive's simulation results are compared to those of the classical and fuzzy DTC-IM drives. The proposed system has been evaluated and found to reduce torque ripple, flux ripple, current harmonics, and total energy consumption by the motor. Further, a comparative simulation study of the above methods at different standard drive cycles is presented. Experimental verification of the proposed algorithm using OPAL-RT is presented. The results represent the superiority of the proposed algorithm compared to the classical DTC and fuzzy DTC IM drives. The torque ripple reduction approach described in this study can also be applied to all induction motors, not only those for electric vehicles or hybrid electric vehicles (HEVs).

Ant Colony Optimized Controller for Fast Direct Torque Control of Induction Motor

Induction motor (IM) drives have tremendous applications as high-performance drives in things such as mine winders, machine tools, electric vehicles, and elevators. Usually, IM drives controlled by direct torque control are preferred for these applications due to their fast torque control and simplicity compared with IM drives with field-oriented control. Proportional–integral–derivative (PID) controllers are commonly used to control IM drives using DTC. Though these controllers are simple and provide excellent response for linear systems with constant set points, they perform poorly with variable set points and IM motor parameter uncertainties. Hence, many control techniques and optimization algorithms have been applied to improve IM drive performance. This paper proposes an IM drive controlled using direct torque control principles, but with the power converter operation optimized to give fast torque performance. The IM drive speed response is improved using an optimized fuzzy PID (FPID). The FPID optimization is accomplished by the ant colony optimization (ACO) algorithm. All components of the IM drive with the optimized control system were simulated using the MATLAB/Simulink platform. The responses of the introduced drive using three different controllers—conventional PID, FPID, and optimized FPID—were compared. The simulation results indicate that the optimized FPID controller provided the best performance in terms of speed and torque. Additionally, the performance of the IM with the proposed optimized FPID under parameter uncertainties was studied. The simulation results indicated the robustness of the optimized FPID controller against parameter uncertainties.

Design and Implementation of an Improved Finite-State Predictive Direct Torque Control for Induction Motor With New Weighting Factor Elimination

Design and Implementation of an Improved Finite-State Predictive Direct Torque Control for Induction Motor With New Weighting Factor Elimination

PI gain optimisation and artificial intelligence based direct torque control of induction motor equipped electric vehicle drives

PI gain optimisation and artificial intelligence based direct torque control of induction motor equipped electric vehicle drives

Design and Xilinx Virtex-field-programmable gate array for hardware in the loop of sensorless second-order sliding mode control and model reference adaptive system–sliding mode observer for direct torque control of induction motor drive

This article aims first to propose an improved space vector modulation–direct torque control in order to enhance the induction motor performance using: (1) a super-twisting controller and (2) a novel model reference adaptive system based on a sliding mode observer. Second, due to the complexity of the suggested control algorithm, this article deals also with a hardware implementation on a field-programmable gate array board. Indeed, the field-programmable gate array is mainly chosen to reduce the execution time, thanks to its parallel processing which significantly improves the quality of the control system by reducing the sampling period, and consequently the delays in the control loop. Besides, the super-twisting controller is proposed to enhance the speed regulation loop which is a second-order sliding mode control technique that uses a continuous control law to prevent the chattering phenomenon induced by the first-order sliding mode control technique. Moreover, the high performance control requires information about the rotor speed which can be obtained by a sensor. Generally, the use of the speed sensor increases the system cost and size, and reduces its system reliability. Therefore, a combination between a model reference adaptive system observer and a sliding mode observer is suggested for speed estimation and overcoming the sensitivity of the classical model reference adaptive system observer against uncertainties and stator resistance variations. The performance of the proposed space vector modulation--direct torque control--super-twisting controller with the model reference adaptive system--sliding mode observer algorithm of an induction motor is verified through simulation, hardware co-simulation and experimental validation utilizing a field-programmable gate array Virtex-5-ML507 board. The performance of the suggested sensorless control method is compared also with other recent published schemes.

Direct Torque Control of Induction Motor Using ConvLSTM Based on Gaussian Pillbox Surface

This article introduces long short-term memory (LSTM)-enabled direct torque control (DTC) for induction motor under a wide range of operation. Low-power applications of industrial drives are more as compared to high-power applications. The main objective of this paper is to address high torque, poor dynamic response, and flux ripple problems observed in low-power induction motor drives. The voltage selector switching table is replaced by LSTM encoder and embedding layer with hysteresis comparator. This will ensure robust control against induction motor disturbances and at the same time will enhance the stator flux trajectory prediction for DTC. Most of the studies describe DTC at a higher speed. In this article, the DTC has been applied to lower speed IM, typically in the range of 100 RPM. Different LSTM models have also been presented in terms of response time. A detailed comparative analysis between LSTM and fuzzy and ANFIS-based DTC has been carried out using MATLAB/Simulink model. The performance has been evaluated under steady and transient conditions as well.

Torque Error Based Auto-tuning of Weighting Factor in Model Predictive Torque Control of Induction Motor Drive

Torque Error Based Auto-tuning of Weighting Factor in Model Predictive Torque Control of Induction Motor Drive

Fuzzy logic direct torque control of induction motor for photovoltaic water pumping system

This study presents a fuzzy logic direct torque control for induction motor to be used in photovoltaic (PV) water pumping system. The system is intended to be less expensive and simple while maximizing PV array power utilization. For this purpose, a smart technology based on fuzzy logic controller (FLC) has been implemented to track the maximum power which has been successfully used in solar water pumping systems under different irradiance levels. Next, to alleviate the disadvantages of standard DTC, we chose fuzzy direct torque control (FDTC) for induction motors driving a centrifugal pump. Furthermore, this study includes the usage of IP with antiwindup and slide mode speed controller (SMC) instead of the traditional proportional-integral speed controller. Finally, the induction motor (IM) speed has been optimized with an optimum speed control in order to maximize the pump’s hydraulic parameters. The FDTC, with its SMC outperforms the photovoltaic pumping system (PVPS) in terms of dynamic performance and robustness. To develop a full simulation model of the proposed PVPS configuration under various climate conditions, MATLAB environment and associated Simulink are used.

Upgrading for overhead crane anti-sway method using variable frequency drive

The paper discusses about upgrading the overhead crane anti-sway method base on the induction motor torque control from rotor resistance starter to variable frequency drives (VFDs). The upgrading included two phases. The phase 1 is to identify the performance of the overhead crane operation on anti-sway method base on the induction motor torque control using rotor resistance starter (old existing motor starter). The phase 2 is to identify the performance of the overhead crane operation on anti-sway method base on the induction motor torque control that use a variable of frequency drive (new upgrading motor starter). The primary equations connecting tractive force and load sway angle, which the motor torque control law is based on is designed for 0% load wobble at the end of the journey. The words accelerating and braking have been written. Outcomes of modelling the behaviour of a trolley-load of two masses for the normal overhead crane load ratios, a system is described weight to the length of the rope, which supports the hypothesis concerning the feasibility of direct load anti-sway control ON and OFF for regulation of motor for overhead cranes.

Electromagnetic torque and reactive torque control of induction motor drives to improve vehicle variable flux operation and torque response

Electromagnetic torque and reactive torque control of induction motor drives to improve vehicle variable flux operation and torque response